📰 Originally published on Securityelites — AI Red Team Education — the canonical, fully-updated version of this article.
🗣️ LLM BASICS FOR BEGINNERS FREE
Day 5 of 5 · 🎉 100% complete!
Four days ago LLMs were mysterious. Now you know the mechanism — predict the next word. You know how they learn — training data, parameters, billions of examples. You know how they fail — hallucinations, bias, knowledge gaps, maths problems. And you know where they’re used — chatbots, agentic systems, writing tools, search summaries.
All of that knowledge is preparation for today. Day 5 is the practical payoff: what do you actually do differently now that you understand LLMs? Every piece of advice here connects back to a mechanism you learned this week. These aren’t generic tech safety tips. They’re specific responses to specific things you now understand about how LLMs work.
By the end of today you’ll have a personal safety framework for LLM use, better prompt-writing skills, and a clear picture of where to go next if you want to keep learning. Let’s finish strong.
🎯 What You’ll Learn in Day 5
A practical verification framework — when and how to check LLM outputs
Privacy rules that actually match how LLMs use your data
How to spot AI-generated scam content before it fools you
Simple techniques that make your prompts dramatically better
What LLMs are genuinely great at vs where to use something else
⏱ 20 min read · 3 exercises · Browser needed for exercises
📋 Full Course Foundation
- Day 1: LLM = predict next word; not thinking; ChatGPT is a product not the LLM
- Day 2: Training data, tokens, parameters, knowledge cutoff
- Day 3: Hallucinations, confidence ≠ accuracy, bias, maths failures
- Day 4: Chatbots, agentic LLMs, system prompts, prompt injection
How to Use LLMs Safely — Day 5 of 5
- The Verification Framework — When to Check, How to Check
- Privacy — What Happens to What You Type
- Spotting AI-Generated Scams — The New Threat
- Writing Better Prompts — Simple Techniques That Actually Work
- What LLMs Are Great At vs Where to Use Something Else
- Where to Go Next
- Questions and Answers
Day 5 is the most directly practical day in the course. All the mechanisms from Days 1–4 are now tools for your own protection. The phishing URL scanner on SecurityElites is a great tool to have bookmarked after today — as LLM-generated phishing becomes more convincing, having a URL scanner in your toolkit becomes more important. And for the next step in your learning: the LLM Hacking Hub is where this course leads.
The Verification Framework — When to Check, How to Check
From Day 3, you know the five categories to always verify. Today I want to go deeper: not just when to verify, but how to do it efficiently without verifying everything every time (which is impractical).
The quick mental filter: Before accepting an LLM output, ask yourself two questions. (1) Does this claim something specific and checkable? (2) Would it matter if this were wrong? If the answer to both is yes — verify. If the claim is general or the stakes are low — use your judgment.
“Explain photosynthesis in simple terms” → general explanation, low stakes if slightly imprecise → no need to verify, just read it.
“The maximum dose of ibuprofen for adults is [specific number] mg” → specific and checkable, high stakes if wrong → verify against a medical source.
How to verify efficiently:
For facts and statistics: run a quick search. If the LLM’s claim matches multiple independent sources, confidence goes up significantly. If you can’t find it independently — be suspicious.
For citations: actually look them up. Don’t assume the book title, author name, or paper citation is real. Search for it. The hallucinated case citations in the lawyer story would have been caught in 30 seconds this way.
For medical, legal, or financial information: authoritative sources only. NHS, Mayo Clinic, or your doctor for medical. Official government websites or a qualified professional for legal. Always treat the LLM as a starting point for understanding terminology and concepts, not a final source for decisions.
For recent events: check a news source with a timestamp. If the LLM confidently describes an event you can’t find coverage of anywhere else — that’s a hallucination signal.
Privacy — What Happens to What You Type
When you type something into an LLM chatbot, where does it go? The answer depends on the platform, but there are some consistent patterns worth knowing.
Your messages go to servers. Unless you’re running an AI model locally on your own device (possible but unusual), your messages travel over the internet to the company’s servers. The LLM processes them there. The response comes back. At every step, the company has technical access to what you typed.
Many platforms use conversations to improve models. By default, ChatGPT, Claude, and Gemini can use your conversations as training data for future model versions — unless you specifically opt out. This means something you wrote casually might influence a future model’s outputs. I find this important enough to have opted out on every platform I regularly use.
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